3,702 research outputs found
A New Approach to Blending and Loading Problem of Molten Aluminum
The problems of blending electrolyzer and multi-constraint optimization of electrolytic aluminum scheduling in the electrolytic aluminum production process were addressed. Based on a mathematical model analysis, a novel hybrid optimization algorithm is proposed for optimization of blending together the molten aluminum in different electrolytic cells. An affinity degree function was designed to represent the path of aluminum scheduling. The mutation operators were designed to implement the transformation of electrolyzer combination and change the route of loading. A typical optimization example from an aluminum plant in northwest China is given in this paper, the results of which demonstrate the effectiveness of the proposed method
Enhanced Gaussian Process Dynamical Models with Knowledge Transfer for Long-term Battery Degradation Forecasting
Predicting the end-of-life or remaining useful life of batteries in electric
vehicles is a critical and challenging problem, predominantly approached in
recent years using machine learning to predict the evolution of the
state-of-health during repeated cycling. To improve the accuracy of predictive
estimates, especially early in the battery lifetime, a number of algorithms
have incorporated features that are available from data collected by battery
management systems. Unless multiple battery data sets are used for a direct
prediction of the end-of-life, which is useful for ball-park estimates, such an
approach is infeasible since the features are not known for future cycles. In
this paper, we develop a highly-accurate method that can overcome this
limitation, by using a modified Gaussian process dynamical model (GPDM). We
introduce a kernelised version of GPDM for a more expressive covariance
structure between both the observable and latent coordinates. We combine the
approach with transfer learning to track the future state-of-health up to
end-of-life. The method can incorporate features as different physical
observables, without requiring their values beyond the time up to which data is
available. Transfer learning is used to improve learning of the hyperparameters
using data from similar batteries. The accuracy and superiority of the approach
over modern benchmarks algorithms including a Gaussian process model and deep
convolutional and recurrent networks are demonstrated on three data sets,
particularly at the early stages of the battery lifetime
Scaling and memory in the return intervals of energy dissipation rate in three-dimensional fully developed turbulence
We study the statistical properties of return intervals between
successive energy dissipation rates above a certain threshold in
three-dimensional fully developed turbulence. We find that the distribution
function scales with the mean return interval as
except for , where the scaling function
has two power-law regimes. The return intervals are short-term and long-term
correlated and possess multifractal nature. The Hurst index of the return
intervals decays exponentially against , predicting that rare extreme
events with are also long-term correlated with the Hurst index
.Comment: 5 pages, 5 figure
Non-Parametric Analyses of Log-Periodic Precursors to Financial Crashes
We apply two non-parametric methods to test further the hypothesis that
log-periodicity characterizes the detrended price trajectory of large financial
indices prior to financial crashes or strong corrections. The analysis using
the so-called (H,q)-derivative is applied to seven time series ending with the
October 1987 crash, the October 1997 correction and the April 2000 crash of the
Dow Jones Industrial Average (DJIA), the Standard & Poor 500 and Nasdaq
indices. The Hilbert transform is applied to two detrended price time series in
terms of the ln(t_c-t) variable, where t_c is the time of the crash. Taking all
results together, we find strong evidence for a universal fundamental
log-frequency corresponding to the scaling ratio . These values are in very good agreement with those obtained in
past works with different parametric techniques.Comment: Latex document 13 pages + 58 eps figure
The position profiles of order cancellations in an emerging stock market
Order submission and cancellation are two constituent actions of stock
trading behaviors in order-driven markets. Order submission dynamics has been
extensively studied for different markets, while order cancellation dynamics is
less understood. There are two positions associated with a cancellation, that
is, the price level in the limit-order book (LOB) and the position in the queue
at each price level. We study the profiles of these two order cancellation
positions through rebuilding the limit-order book using the order flow data of
23 liquid stocks traded on the Shenzhen Stock Exchange in the year 2003. We
find that the profiles of relative price levels where cancellations occur obey
a log-normal distribution. After normalizing the relative price level by
removing the factor of order numbers stored at the price level, we find that
the profiles exhibit a power-law scaling behavior on the right tails for both
buy and sell orders. When focusing on the order cancellation positions in the
queue at each price level, we find that the profiles increase rapidly in the
front of the queue, and then fluctuate around a constant value till the end of
the queue. These profiles are similar for different stocks. In addition, the
profiles of cancellation positions can be fitted by an exponent function for
both buy and sell orders. These two kinds of cancellation profiles seem
universal for different stocks investigated and exhibit minor asymmetry between
buy and sell orders. Our empirical findings shed new light on the order
cancellation dynamics and pose constraints on the construction of order-driven
stock market models.Comment: 17 pages, 6 figures and 6 table
New Evidence of Discrete Scale Invariance in the Energy Dissipation of Three-Dimensional Turbulence: Correlation Approach and Direct Spectral Detection
We extend the analysis of [Zhou and Sornette, Physica D 165, 94-125, 2002]
showing statistically significant log-periodic corrections to scaling in the
moments of the energy dissipation rate in experiments at high Reynolds number
() of three-dimensional fully developed turbulence. First, we
develop a simple variant of the canonical averaging method using a rephasing
scheme between different samples based on pairwise correlations that confirms
Zhou and Sornette's previous results. The second analysis uses a simpler local
spectral approach and then performs averages over many local spectra. This
yields stronger evidence of the existence of underlying log-periodic
undulations, with the detection of more than 20 harmonics of a fundamental
logarithmic frequency corresponding to the preferred
scaling ratio .Comment: 9 RevTex4 papes including 8 eps figure
A surrogate modelling approach based on nonlinear dimension reduction for uncertainty quantification in groundwater flow models
In this paper, we develop a surrogate modelling approach for capturing the output field (e.g., the pressure head) from groundwater flow models involving a stochastic input field (e.g., the hy- draulic conductivity). We use a Karhunen-Lo`eve expansion for a log-normally distributed input field, and apply manifold learning (local tangent space alignment) to perform Gaussian process Bayesian inference using Hamiltonian Monte Carlo in an abstract feature space, yielding outputs for arbitrary unseen inputs. We also develop a framework for forward uncertainty quantification in such problems, including analytical approximations of the mean of the marginalized distri- bution (with respect to the inputs). To sample from the distribution we present Monte Carlo approach. Two examples are presented to demonstrate the accuracy of our approach: a Darcy flow model with contaminant transport in 2-d and a Richards equation model in 3-d
Recurrence interval analysis of high-frequency financial returns and its application to risk estimation
We investigate the probability distributions of the recurrence intervals
between consecutive 1-min returns above a positive threshold or
below a negative threshold of two indices and 20 individual stocks in
China's stock market. The distributions of recurrence intervals for positive
and negative thresholds are symmetric, and display power-law tails tested by
three goodness-of-fit measures including the Kolmogorov-Smirnov (KS) statistic,
the weighted KS statistic and the Cram\'er-von Mises criterion. Both long-term
and shot-term memory effects are observed in the recurrence intervals for
positive and negative thresholds . We further apply the recurrence interval
analysis to the risk estimation for the Chinese stock markets based on the
probability , Value-at-Risk (VaR) analysis and VaR analysis
conditioned on preceding recurrence intervals.Comment: 17 pages, 10 figures, 1 tabl
Determinants of immediate price impacts at the trade level in an emerging order-driven market
The common wisdom argues that, in general, large trades cause large price
changes, while small trades cause small price changes. However, for extremely
large price changes, the trade size and news play a minor role, while the
liquidity (especially price gaps on the limit order book) is a more influencing
factor. Hence, there might be other influencing factors of immediate price
impacts of trades. In this paper, through mechanical analysis of price
variations before and after a trade of arbitrary size, we identify that the
trade size, the bid-ask spread, the price gaps and the outstanding volumes at
the bid and ask sides of the limit order book have impacts on the changes of
prices. We propose two regression models to investigate the influences of these
microscopic factors on the price impact of buyer-initiated partially filled
trades, seller-initiated partially filled trades, buyer-initiated filled
trades, and seller-initiated filled trades. We find that they have
quantitatively similar explanation powers and these factors can account for up
to 44% of the price impacts. Large trade sizes, wide bid-ask spreads, high
liquidity at the same side and low liquidity at the opposite side will cause a
large price impact. We also find that the liquidity at the opposite side has a
more influencing impact than the liquidity at the same side. Our results shed
new lights on the determinants of immediate price impacts.Comment: 21 IOP tex pages including 5 figures and 5 tables. Accepted for
publication in New Journal of Physic
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